About Me:

I am a PhD Biologist and aspiring data scientist working as a research geneticist. Currently, my research focuses on using deep learning to predict maize yield from genetic, environmental, and management data. Previously, I focused on compensation of neural circuits to aberrant activity.

I have experience with statistical modeling, machine learning, deep learning, experimental design, and data visualization & communication.

• : 5.75 years of R Programming experience using tidyverse, lme4, caret, ggplot2, & shiny.
• : 1.24 years of Python programming experience using pandas, plotly, keras.
• : Experience with bash, computing clusters, virtual environments (conda, singularity, docker), version control (git), literate programming (Rmarkdown, Jupyter).

PhD. Biological Sciences

University of Missouri, Columbia, MO

N/A

2021 - 2015

• Coursework included Machine Learning Methods for Biomedical Informatics, Quantitative Methods in the Life Sciences, and Grant Writing

Bachelor of Science

Truman State University, Kirksville, MO

N/A

2015 - 2011

• Coursework included Next Generation Sequence Data and Analysis, Bioinformatics, Analysis of Variance and Experimental Design, Non-Parametric Statistics, and Economic & Medicinal Botany

Research Geneticist

Jacob Washburn Lab, USDA-ARS

N/A

Present - 2021

• Improved yield prediction of maize in diverse environments by using deep learning to better capture gene by environment effects. Conducted research and wrote manuscript on results. Communciated results to stakeholders through local and national presentations. Supervised undergraduate high throughput phenotyping project and assisted with related coding needs.

Graduate Student

David Schulz Lab, Missouri State University

N/A

2021 - 2016

• Assessed the efficacy of machine learning models to recapitulate neural cell identity from mRNA expression 1. Demonstrated that activity desynchronization induces degree dependent changes in conductance between neurons 2. Investigated the compensatory effects of elevated depolarization on neuronal excitability, conductances, and ion channel mRNA abundances in small neural networks 3.

J. Perry Gustafson Award for Outstanding Graduate Research in the Life Sciences

This award is granted in recongition of the quality of their independent research and academic achievements. Reciepients receive a $2,000 award.

N/A

2019

NIH T32 Training Grant Recipient

This fellowship provides a $27,000 yearly stipend and two travel awards of $750 to facilitate presenting research at scientific conferences.

N/A

2018 - 2016

Software Carpentries Certified Instructor

Received theoretical and practical instruction on leading computational workshops. Taught R for Reproducible Scientific Analysis, assisted in teaching Data Management with SQL.

N/A

2022

Panel Member, Next-Generation Omics, Biological Sciences Divisional Retreat

Ruthie Angelovici, David J Schulz, Daniel R Kick, and Mannie Liscum, University of Missouri Division of Biological Sciences Retreat

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2022

Workshop Creator; Tools and Techniques for a Jupyter Based Scientific Workflow

Created and delivered a workshop on data visualization in Python for University of Missouri Bioinformatics in Plant Science

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2022

Mentoring Undergraduate Researchers

As a Postdoc: Supervised 2 students conducting a highthroughput root phenotyping experiment: Grace Sidberry (2021-pres.), Madi Michell (2022-pres.). As a PhD Student: Trained 5 students in electrophysiolgy techniques and oversaw their projects. Abby Beckerdite (2016-2019), Ayla Ross (2019), Katlyn Sullivan (2018), Kelly Hiersche (2017), & Rody Kingston (2016)

N/A

2022 - 2016

Lead Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

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2021 - 2020

• Coordinated adaptation and expansion of lab material to be fully online due to Covid-19. Developed and deployed a statistics web application used by a minimum 705 students as of 2021 source , deployed. Includes capability for visualization, testing model assumptions, frequentist models, non-parametric tests, basic Bayesian models.

Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

N/A

2020 - 2018

• Tested additional curriculum alterations, tested grade distrubutions to identify and adjust for grader effects.

Curriculum Consultant, Animal Physiology Lab

Biological Sciences, University of Missouri

N/A

2018

• Updated curriculum and redesigned experiments placing a greater focus primary literature and data analysis.

Teaching Assistant, Animal Physiology Lab

Biological Sciences, University of Missouri

N/A

2016 - 2015

• Provided weekly lectures on relevant background, ensured experiments were conducted safely, provided timely feedback on assignments.

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Daniel R. Kick, Jason G. Wallace, James C. Schnable, Judith M. Kolkman, Bar?? Alaca, Timothy M. Beissinger, David Ertl, Sherry Flint-Garcia, Joseph L. Gage, Candice N. Hirsch, Joseph E. Knoll, Natalia de Leon, Dayane C. Lima, Danilo Moreta, Maninder P. Singh, Teclemariam Weldekidan, Jacob D. Washburn

bioRxiv (Under-Review)

2022

Timing dependent potentiation and depression of electrical synapses contributes to network stability in the crustacean cardiac ganglion

Daniel R. Kick and David J. Schulz

The Journal of Neuroscience

2022

Molecular profiling of single neurons of known identity in two ganglia from the crab Cancer borealis

Adam J. Northcutt1, Daniel R. Kick1, Adriane G. Otopalik, Benjamin M. Goetz, Rayna M. Harris, Joseph M. Santin, Hans A. Hofmann, Eve Marder, and David J. Schulz

Proceedings of the National Academy of Sciences

2019

Yield Prediction Accuracy is Improved Through Incorporating Genetic, Environmental, and Management Interactions with Deep Learning,

University of Missouri Interdisciplinary Plant Group Symposium, Columbia MO

N/A

2022

• Daniel R Kick, Jacob D. Washburn (2022)
• Presentation

Effects of blockade of K+ currents on membrane conductance and channel expression at 1 hour and 24 hours in motor neurons of the cardiac ganglion.

Dynamic Neural Networks: The Stomatogastric Nervous System, Virtual

N/A

2020

• Daniel R Kick, Brian J. Lane, David J Schulz.
• Presentation

Loss of synchronous activity across gap junctions results in a phase-dependent change in coupling conductance magnitude.

Neuroscience, Chicago IL

N/A

2019

• Daniel R Kick, David J Schulz.
• Poster

Variability from mRNAs to network output in the C. borealis cardiac ganglion.

Dynamic Neural Networks: The Stomatogastric Nervous System, Washington D.C

N/A

2017

• Daniel R Kick, David J Schulz.
• Presentation

Variation across network output, excitatory post synaptic potentials, ionic conductances, and ion channel and receptor mRNAs within motor neurons of the crustacean cardiac ganglion.

Neuroscience, Washington D.C.

N/A

2017

• Daniel R Kick, Brian J Lane, Joseph L Ransdell, Satish S Nair, David J Schulz.
• Poster

From Neurobiologist to Research Geneticist

Beyond the PhD (beyond-the-phd.com), Virtual

N/A

2022

• Daniel R Kick
• Presentation